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docs(streaming): A100/H100 NVDEC cannot decode AV1 — correct guidance

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NVIDIA's decode support matrix: the compute GPUs A100 (GA100) and H100 (GH100) have no
AV1 NVDEC decoder; only Ada (L4/L40/RTX40) and some Ampere (A10/A40/A16) do. So on
A100/H100 nodes, AV1 datasets must be decoded on CPU or re-encoded to H.265/H.264 — no
torchcodec build enables cuda AV1 decode there. Also distinguish that error from
"Unsupported device: cuda (variant: ffmpeg)", which is a torchcodec-built-without-CUDA
issue. Update diagnose_decode.py message + benchmark README accordingly.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Pepijn
2026-06-09 17:08:54 +02:00
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@@ -55,6 +55,15 @@ limited number of concurrent decode sessions per GPU; if you hit session/IPC lim
or compare against `--num_workers 0` (single-process NVDEC, which often saturates the decode engine on its
own). Result files include the decode device in their name (`..._w6_cuda.json`).
> **Codec ⇄ NVDEC compatibility (important).** NVDEC can only decode codecs its hardware supports. LeRobot
> v3 datasets are often **AV1**-encoded, and the **A100 and H100 compute GPUs have no AV1 NVDEC decoder**
> (per NVIDIA's [decode support matrix](https://developer.nvidia.com/video-encode-and-decode-gpu-support-matrix-new));
> only Ada (L4/L40/RTX40) and a few Ampere cards (A10/A40/A16) do. On A100/H100, AV1 must be decoded on
> **CPU**, or the dataset re-encoded to H.265/H.264 (which those GPUs' NVDEC do support). Run
> `diagnose_decode.py --video_decode_device cuda` to check your exact node before relying on `cuda` decode.
> A `cuda` torchcodec build also needs an FFmpeg with NVDEC; see
> <https://github.com/meta-pytorch/torchcodec#installing-cuda-enabled-torchcodec>.
Reference data root: bucket sources resolve through `--data_files_root hf://buckets/<owner>/<name>` (metadata
still loads from `--repo_id`). The local `single`/`sarm` CPU baselines on this dataset were ~176 / ~212
frames/s/node at `--num_workers 3` (3 cameras, fps 20).